Outdoor mesocosm experiments to improve understanding of risks to environmental health
Bibliographic record
Abstract
Abstract Mesocosms are outdoor experimental systems designed to separate and test environmental responses, in this case, cumulative effects to field-collected periphyton and macroinvertebrate communities from multiple stressors. This experimental system produces valuable, highly reproducible data, and along with field monitoring, laboratory bioassays and modeling results, provides a strong weight-of-evidence approach for aquatic risk assessment. Through the control of confounding environmental variables, this type of experiment permits the separation of interactions between multiple stressors in complex effluents or due to shifting ambient conditions. The system described is modular and was originally developed to facilitate transport of the entire system to remote or industrial test sites (e.g., for in situ 21-d chronic level testing). However, this setup is also appropriate for long term installations with suitable provision for routine maintenance and losses associated with regular wear-and-tear on equipment. The following protocol details a basic stream mesocosm system setup (e.g., 4 replicate streams per treatment-level) which is the foundation for the more than a dozen stream mesocosm experiments conducted by the authors since the 1990s.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".